{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Optimization Objectives"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "NeuralForecast is a highly modular framework capable of augmenting a wide variety of robust neural network architectures with different point or probability outputs as defined by their optimization objectives.\n",
    "\n",
    "## Point losses \n",
    "\n",
    "| Scale-Dependent                                              | Percentage-Errors                                                     | Scale-Independent                                              | Robust                                                 |\n",
    "|:-------------------------------------------------------------|:----------------------------------------------------------------------|:---------------------------------------------------------------|:-------------------------------------------------------|\n",
    "|[**MAE**](../../losses.pytorch.html#mean-absolute-error-mae)       |[**MAPE**](../../losses.pytorch.html#mean-absolute-percentage-error-mape)   |[**MASE**](../../losses.pytorch.html#mean-absolute-scaled-error-mase)|[**Huber**](../losses.pytorch.html#huber-loss)            |\n",
    "|[**MSE**](../../losses.pytorch.html#mean-squared-error-mse)        |[**sMAPE**](../../losses.pytorch.html#symmetric-mape-smape)                 |                                                                |[**Tukey**](../../losses.pytorch.html#tukey-loss)            |\n",
    "|[**RMSE**](../../losses.pytorch.html#root-mean-squared-error-rmse) |                                                                       |                                                                |[**HuberMQLoss**](../../losses.pytorch.html#huberized-mqloss)|\n",
    "\n",
    "## Probabilistic losses\n",
    "\n",
    "|Parametric Probabilities                                      | Non-Parametric Probabilities                                 |\n",
    "|:-------------------------------------------------------------|:-------------------------------------------------------------|\n",
    "|[**Normal**](../../losses.pytorch.html#distributionloss)           |[**QuantileLoss**](../../losses.pytorch.html#quantile-loss)        |\n",
    "|[**StudenT**](../../losses.pytorch.html#distributionloss)          |[**MQLoss**](../../losses.pytorch.html#multi-quantile-loss-mqloss) |\n",
    "|[**Poisson**](../../losses.pytorch.html#distributionloss)          |[**HuberQLoss**](../../losses.pytorch.html#huberized-quantile-loss)|\n",
    "|[**Negative Binomial**](../../losses.pytorch.html#distributionloss)|[**HuberMQLoss**](../../losses.pytorch.html#huberized-mqloss)      |\n",
    "|[**Tweedie**](../../losses.pytorch.html#distributionloss)          |[**IQLoss**](../../losses.pytorch.html#iqloss)  |\n",
    "|[**PMM**](../../losses.pytorch.html#poisson-mixture-mesh-pmm) | [**HuberIQLoss**](../../losses.pytorch.html#huberized-iqloss)|\n",
    "|[**GMM**](../../losses.pytorch.html#gaussian-mixture-mesh-gmm) | [**ISQF**](../../losses.pytorch.html#isqf)  |\n",
    "|[**NBMM**](../../losses.pytorch.html#negative-binomial-mixture-mesh-nbmm) | |"
   ]
  }
 ],
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 "nbformat": 4,
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}
